CN114005276B - Expressway congestion early warning method based on multi-data source fusion - Google Patents
Expressway congestion early warning method based on multi-data source fusion Download PDFInfo
- Publication number
- CN114005276B CN114005276B CN202111239862.6A CN202111239862A CN114005276B CN 114005276 B CN114005276 B CN 114005276B CN 202111239862 A CN202111239862 A CN 202111239862A CN 114005276 B CN114005276 B CN 114005276B
- Authority
- CN
- China
- Prior art keywords
- congestion
- section
- speed
- vehicle
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Emergency Management (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention provides a highway congestion early warning method based on multi-data-source fusion, which realizes six-dimensional real-time data analysis from congestion index, vehicle speed, congestion mileage, congestion tendency, congestion reason and section flow by fusing effective data such as road conditions, high-speed geographic information, free flow speed, free flow rate, high-speed events and the like and utilizing a clustering algorithm and a big data real-time flow analysis technology, and has the advantages of wider data source and higher accuracy. In addition, in order to enable high-speed monitoring personnel to know the running condition of the highway in real time and push the congestion information in real time, the operation informatization level of the highway is improved.
Description
Technical Field
The invention belongs to the technical field of traffic information, and particularly relates to an expressway congestion early warning method based on multi-data-source fusion.
Background
With the continuous development of economy, the quantity of motor vehicles kept continuously increases, the traffic volume of a road network rapidly and stably rises, and the traffic volume of a main busy high-speed road section tends to be saturated. When the public goes out, the public is concerned about a series of problems such as blockage at high speed, blockage where, blockage at what time, blockage duration, and reasons. The real-time detection of the highway congestion early warning is one of the core problems with great difficulty in the field of intelligent transportation at present, but the existing traditional detection methods are insufficient in precision and instantaneity. Map operators in China, such as high-grade, hundredth, tencent and the like, can only know rough congestion positions and have great defects for highway management. On one hand, the accuracy of the jam position is not enough, and the jam position can not be connected with professional high-speed services. On the other hand, the operator has no event, the accident data cannot know the original reason of the congestion, and the vehicle management and control and the congestion management cannot be carried out.
Disclosure of Invention
The invention aims to solve the technical problems and provides a highway congestion early warning method based on multi-data source fusion.
In order to achieve the purpose, the invention adopts the following technical scheme:
a highway congestion early warning method based on multi-data source fusion comprises the following steps:
s1, passing vehicle information data are acquired through a high-speed portal system, two to three portal devices are arranged on a high-speed road section, a section is arranged between every two adjacent portals, the portals read and record information of vehicles passing through the portals in real time and upload the information to a big data platform, the big data platform is used for analyzing, the passing vehicle information data acquired by the high-speed portal system are processed in real time, and section equivalent flow and section weighted vehicle speed are calculated;
s2, using a congestion discriminator to obtain the level of congestion according to the section equivalent flow and the section weighted vehicle speed calculated by the big data platform;
s3, converting the longitude and latitude information of the geographic coordinates into a pile number of a high-speed road section by using a high-speed geographic information engine through the association data of the pile number of the portal and the longitude and latitude coordinates, judging the congestion possibility of the road through an algorithm by using a congestion early warning service interface, judging congestion events and congestion road conditions in an auxiliary manner, combining the congestion, accidents and construction data of the high-speed events with the reported construction information of a management center and the road congestion and accident information association congestion results, and obtaining congestion association results;
and S4, pushing the congestion event to a monitoring working platform in real time, and pushing congestion information to department personnel of different levels according to the level of the congestion event.
As a preferred technical solution, in S1, the method for calculating the cross-sectional equivalent flow rate is as follows: the vehicle is divided into a first class, a second class, a third class, a fourth class, a fifth class and a sixth class according to the vehicle type, the section traffic flow of different vehicle types is converted into section equivalent flow through equivalent conversion, and the conversion rule is as follows: the equivalent transformation coefficient of the first class of passenger car is 1, the equivalent transformation coefficient of the third class of passenger car is 1.5, the transformation coefficient of the first class of freight car is 1, the transformation coefficient of the second class of freight car is 1.5, the transformation coefficient of the fourth class of freight car is 2.5, and the transformation coefficient of the fifth class of freight car is 4.
As a preferable technical solution, S1 further includes: all vehicle passing records passing through the current section are obtained from the high-speed portal system, the vehicle records counted after high-speed special service are filtered, and the section free flow is calculated: the sum of the equivalent flow of the sections converted by various vehicle types is the free flow of the section.
As a preferable technical solution, in S1, the method for calculating the cross-section weighted vehicle speed includes: the method comprises the steps that each portal frame has a corresponding stake number to represent the geographical position of the portal frame on a highway, the absolute value of the difference value of the stake numbers of two adjacent portal frames is the total length of a section, the time of a vehicle passing through the portal frame is obtained from a high-speed portal frame system, the absolute value of the time difference value of the vehicle passing through the two adjacent portal frames is the total time of the vehicle passing through the section, the passing distance is divided by the passing time to obtain the passing speed, the passing speed is obtained by accumulating the passing vehicle speed of all vehicles in the period and dividing the total number of the vehicles passing in the period by the total number of the vehicles in the period aiming at different vehicle types, the average vehicle speed obtained by accumulating different vehicle types is multiplied by equivalent flow corresponding to the vehicle type and then divided by the sum of the equivalent flow of all the vehicle types to obtain the weighted vehicle speed of the section.
As a preferable technical solution, in S2, the method for calculating the congestion level includes: carrying out congestion judgment by matching the section free flow vehicle speed and the free flow vehicle flow with congestion data provided by a map operator; when the section flow is between the threshold and 1.1 times of the threshold and the section vehicle speed is between 30km/h and 60km/h, primary congestion is judged, or primary congestion can be judged when the congestion mileage provided by a map operator is less than or equal to 2 km; when the section flow is between 1.1 and 1.2 times of threshold values and the section vehicle speed is between 10km/h and 30km/h, determining the second-level congestion, or determining the second-level congestion when the congestion mileage provided by a map operator is more than 2 km and less than or equal to 5 km; and when the section flow is more than 1.2 times of the threshold value and the section vehicle speed is less than or equal to 10km/h, judging the three-level congestion, or judging the three-level congestion when the congestion mileage provided by a map operator is more than 5 km.
As a preferable technical scheme, in S3, the congestion probability of the road and the length of the road segment occupied by the congestion event are judged based on an algorithm, and the geographical longitude and latitude information is converted into the stake number of the high-speed road segment by combining with a high-speed geographical information engine to assist in judging the congestion level.
As a preferable technical solution, in S3, the high-speed event associates the stake mark of the congestion position with the traffic event position to obtain the traffic congestion cause, and obtains the congestion association result by combining the congestion result.
As an optimal technical scheme, the highway congestion early warning method based on multi-data source fusion further comprises the following steps:
a1, calculating and analyzing in real time;
a2, when the congestion level is judged to reach the first level, early warning corresponding information is pushed to the branch center, monitoring personnel and the shift leader, program judgment is continued, and if the congestion level does not meet the condition, the step A1 is returned to;
a3, when the congestion level is judged to reach the second congestion level, a corresponding message is early warned and pushed to the subsidiary master assistant and the subsidiary central master, the program is judged to continue, and if the condition is not met, the step A1 is returned to;
and A4, when the congestion level is judged to reach the third level, early warning corresponding messages are pushed to the branch center master and the center leader, and the cycle is ended. Returning to the step A1 when the condition is not met;
and A5, repeatedly circulating the steps A1, A2, A3 and A4.
As a preferred technical scheme, the early warning principle is that the early warning is carried out once when the time point 1 reaches a threshold value, no early warning is carried out when the time point is lower than the threshold value, data in a near segment are counted in real time, early warning is carried out once a condition that the congestion level is met is detected, and after information is reported, the early warning is not carried out again if the congestion level is not met for a period of time;
after the time point 1 is reported, if the flow fluctuates up and down on the threshold value or continuously increases and meets the first-level condition of congestion, repeated early warning is carried out every 30 minutes, the current value and the early warning value at the last time are compared and displayed during early warning, and the early warning is immediately carried out on the condition that the flow threshold value which increases suddenly is higher than the threshold value by 10 percent and meets the first-level condition of congestion.
After the technical scheme is adopted, the invention has the following advantages:
according to the highway congestion early warning method based on the fusion of multiple data sources, effective data such as road conditions, high-speed geographic information, free flow speed, free flow, high-speed events and the like are fused, a clustering algorithm and a big data real-time flow analysis technology are utilized, six-dimensional real-time data analysis from congestion indexes, vehicle speed, congestion mileage, congestion tendency, congestion reasons and section flow is achieved, the data sources are wide, and the accuracy is high. In addition, in order to enable high-speed monitoring personnel to know the running condition of the highway in real time and push the congestion information in real time, the operation informatization level of the highway is improved.
Drawings
FIG. 1 is an architecture diagram of a highway congestion early warning method based on multi-data source fusion;
FIG. 2 is a flow chart of a highway congestion early warning method based on multi-data source fusion;
FIG. 3 is a flow chart of a warning method;
FIG. 4 is an architecture diagram of a highway congestion warning system;
FIG. 5 is a system display page of the highway congestion warning system;
fig. 6 is a condition screening function page of the highway congestion warning system;
FIG. 7 is a near-one-hour congestion information page for a highway congestion warning system;
fig. 8 is a block-prone position ranking page of the highway congestion early warning system;
fig. 9 is a cross-sectional flow ranking page of the highway congestion warning system;
fig. 10 is a flow trend monitoring page of the highway congestion warning system;
fig. 11 is a real-time screenshot of the robot real-time push.
Detailed Description
The present invention will be described in further detail with reference to the following drawings and specific examples.
As shown in fig. 1-2, a highway congestion early warning method based on multi-data source fusion includes the following steps:
s1, acquiring passing vehicle information data through a high-speed portal system, arranging two to three portal devices on a high-speed road section, arranging a section between every two adjacent portals, reading and recording information of vehicles passing through the portals in real time, uploading the information to a big data platform, analyzing the information by using the big data platform, processing the passing vehicle information data acquired by the high-speed portal system in real time, and calculating section equivalent flow and section weighted vehicle speed. The section equivalent flow refers to the standard flow converted according to the equivalent conversion standard of the number of all vehicle types collected by the free flow portal frame in unit time. The section weighted vehicle speed refers to the average speed obtained by calculating according to vehicle model data and equivalent flow data obtained through the free flow portal.
The method for calculating the cross section equivalent flow comprises the following steps: the vehicle is divided into a first class, a second class, a third class, a fourth class, a fifth class and a sixth class according to the type of the vehicle, the section traffic flow of different vehicle types is converted into section equivalent flow through equivalent conversion, and the conversion rule is as follows: the equivalent transformation coefficient of the first class of passenger car is 1, the equivalent transformation coefficient of the third class of passenger car is 1.5, the transformation coefficient of the first class of freight car is 1, the transformation coefficient of the second class of freight car is 1.5, the transformation coefficient of the fourth class of freight car is 2.5, and the transformation coefficient of the fifth class of freight car is 4.
All vehicle passing records passing through the current section are obtained from the high-speed portal system, the vehicle records counted after high-speed special service are filtered, and the free flow of the section is calculated: the sum of the equivalent flow of the sections converted by various vehicle types is the free flow of the section. Assuming that k1, k2, k3, k4 is the number of classes one to four of passenger cars and h1, h2, h3, h4, h5, h6 is the number of classes one to six of trucks, the free stream flow = (k 1+ k 2) × 1+ (k 3+ k 4) × 1.5 ++ h1 × 1+ (h 2+ h 3) × 1.5 ++ h4 × 2.5+ (h 5+ h 6) × 4.
The method for calculating the section weighted vehicle speed comprises the following steps: the method comprises the steps that each portal frame has a corresponding stake number to represent the geographical position of the portal frame on a highway, the absolute value of the difference value of the stake numbers of two adjacent portal frames is the total length of a section, the time of a vehicle passing through the portal frame is obtained from a high-speed portal frame system, the absolute value of the time difference value of the vehicle passing through the two adjacent portal frames is the total time of the vehicle passing through the section, the passing distance is divided by the passing time to obtain the passing speed, the passing speed is obtained by accumulating the passing vehicle speed of all vehicles in the period and dividing the total number of the vehicles passing in the period by the total number of the vehicles in the period aiming at different vehicle types, the average vehicle speed obtained by accumulating different vehicle types is multiplied by the equivalent flow rate corresponding to the vehicle type and then divided by the sum of the equivalent flow rate of all vehicle types to obtain the weighted vehicle speed of the section, namely the free flow vehicle speed. Assuming that Vk1, vk2, vk3 and Vk4 are average speeds of first to fourth classes of passenger cars and Vh1, vh2, vh3, vh4, vh5 and Vh6 are average speeds of first to sixth classes of trucks, the following vehicle speed ranges are obtained
Wherein the bus model i =1,2,3,4, the truck model j =1,2,3,4,5,6。
And S2, using a congestion discriminator to obtain the level of congestion according to the section equivalent flow and the section weighted vehicle speed calculated by the big data platform.
The calculation method of the congestion level comprises the following steps: carrying out congestion judgment by matching the section free flow vehicle speed and the free flow vehicle flow with congestion data provided by a map operator; when the section flow is between the threshold and 1.1 times of the threshold and the section vehicle speed is between 30km/h and 60km/h, primary congestion is judged, or primary congestion can be judged when the congestion mileage provided by a map operator is less than or equal to 2 km; when the section flow is between 1.1 and 1.2 times of threshold values and the section vehicle speed is between 10km/h and 30km/h, determining the second-level congestion, or determining the second-level congestion when the congestion mileage provided by a map operator is more than 2 km and less than or equal to 5 km; and when the section flow is more than 1.2 times of the threshold value and the section vehicle speed is less than or equal to 10km/h, judging the three-level congestion, or judging the three-level congestion when the congestion mileage provided by a map operator is more than 5 km.
And S3, converting the longitude and latitude information of the geographic coordinates into the pile number of a high-speed road section by using a high-speed geographic information engine through the correlation data of the pile number of the portal and the longitude and latitude coordinates of the geographic coordinates, judging the congestion possibility of the road through an algorithm by using a high-speed OpenAPI congestion early warning service interface, judging congestion events and congestion road conditions in an auxiliary manner, combining the congestion, accidents and construction data of the high-speed events with the reported construction information of a management center and the road section congestion and accident information to correlate congestion results, and obtaining congestion correlation results.
The congestion road condition data is road condition data issued by the provincial delivery monitoring center, and the accuracy of the data is explained by the provincial delivery monitoring center.
The high-speed geographic information engine provides geographic information data, the geographic information data is used for collecting professional geographic information data of the highway for provincial delivery, and error-free conversion of longitude and latitude and pile numbers is achieved.
The Congestion early warning service interface of the GoodOpenAPI is used for providing operator data, namely Congestion data of Goods, and the accuracy of the data is explained by the Goods.
The Gord road condition judges the congestion probability of the road and the length of the road section occupied by the congestion event based on an algorithm, and converts the geographical longitude and latitude information into the pile number of the high-speed road section by combining a high-speed geographical information engine so as to assist in judging the congestion level. And the high-speed event correlates the pile number of the congestion position with the traffic event position to obtain the reason of the traffic congestion, and the congestion correlation result is obtained by combining the congestion result.
And S4, pushing the congestion event to a monitoring working platform in real time, and pushing congestion information to department personnel of different levels according to the level of the congestion event. In this embodiment, the jam message is pushed by the staple monitoring robot.
In order to enable high-speed management personnel and monitoring personnel to know the running condition of the highway in real time and push the congestion information in real time, rules and flows of early warning are formulated.
As shown in fig. 3, the early warning method includes:
a1, calculating and analyzing in real time;
a2, when the congestion level is judged to reach the first congestion level, pre-warning corresponding information to be pushed to a branch center, monitoring personnel and a shift leader, continuing program judgment, and returning to the step A1 if the condition is not met;
a3, when the congestion level is judged to reach the second congestion level, pre-warning corresponding messages to be pushed to the subsidiary master assistant and the subsidiary central master, continuing program judgment, and returning to the step A1 if the conditions are not met;
and A4, when the congestion level is judged to reach the third level, pre-warning corresponding messages to be pushed to the branch center master and the center leader, and ending the cycle. Returning to the step A1 when the condition is not met;
and A5, repeatedly circulating the steps A1, A2, A3 and A4.
The early warning principle is that the early warning is carried out once when the time point 1 reaches a threshold value, no early warning is carried out when the time point is lower than the threshold value, data in a near segment are counted in real time, early warning is carried out once the condition that the congestion level is met is detected, and after information is reported, early warning is not carried out again if the congestion level is not met for a period of time;
after the time point 1 is reported, if the flow fluctuates up and down on the threshold value or continuously increases and meets the first-level condition of congestion, repeated early warning is carried out every 30 minutes, the current value and the early warning value at the last time are compared and displayed during early warning, and the early warning is immediately carried out on the condition that the flow threshold value which increases suddenly is higher than the threshold value by 10 percent and meets the first-level condition of congestion.
The highway congestion early warning method based on multi-data source fusion, disclosed by the invention, has the advantages that effective data such as high-speed road conditions, high-speed geographic information, free flow speed, section weighted vehicle speed, free flow, section equivalent flow, high-speed events and the like are fused, a clustering algorithm and a big data real-time flow analysis technology are utilized, six-dimensional real-time data analysis including a congestion index, a vehicle speed, congestion mileage, a congestion trend, a congestion reason and section flow is realized, the data source degree is wider, and the accuracy is higher. The whole system is flexible in configuration, automatic in detection and automatic in early warning.
Based on the highway congestion early warning method based on the multi-data source fusion, the highway congestion early warning system is developed in the embodiment. The front end of a technical frame of the system adopts technologies such as Jquery, VUE, CSS3, HTML5, javaScript and the like; the background uses a Mybatis, mysql, springBoot and SpringMVC result database; big data analysis techniques used Kudu, sparkStreaming, sparkSQLStreamSet, impala, hive, etc. big data components for analytical and collaborative. The whole system architecture can be transversely expanded, and the development language uses mainstream Java and Scala plasticity and has higher maintenance ratio. The system supports distributed deployment and remote operation and maintenance, can process TB-level data quantity, has response time at the level of seconds, and has a system architecture diagram as shown in FIG. 4 and a system display page as shown in FIG. 5.
The highway congestion early warning system has a condition screening function, the condition screening function is mainly used for screening congestion positions and areas, and congestion information areas can be screened through the areas after screening, so that congestion early warning data concerned in each area or management position can be screened out. The pages for the conditional screening function are shown in figure 6.
The expressway congestion early warning system is provided with a display page of congestion information of nearly one hour, a congestion road section of nearly one hour is mainly displayed in a congestion information module of nearly one hour, and the displayed information mainly comprises 11 important parameters including a sequence number, a congestion index, a section, a direction, a position, a congestion mileage, an average speed, a flow, a congestion reason, a congestion trend and congestion time. The page can dynamically refresh data, and once the information of highway congestion is found, early warning is given to relevant responsible personnel in time. The congestion information display for the last hour is shown in fig. 7.
The highway congestion early warning system is divided into three layers of abnormal congestion, severe congestion and light congestion according to congestion levels from high to low, and the three colors of the abnormal congestion, the severe congestion and the light congestion correspond to red, yellow and blue. According to the captured multidimensional data analysis, the system can automatically identify and judge the congestion level of each road section and directly issue the early warning message to managers through the intelligent robot in the background at the first time, so that the data deviation in the data transfer process is effectively reduced. In addition, the system integrates traffic event data, the traffic post numbers are matched to obtain congestion reason data, and the reason of congestion is tracked in real time at the first time.
The expressway congestion early warning system is provided with a display page for arranging easily blocked positions, the easily blocked positions are mainly displayed in an easily blocked position arranging module, the section name of the front 10 of the arrangement is taken and refreshed in real time, the section name, the direction and the ranking grade of displayed fields are shown, and the first description of the ranking is the most severe congestion. The row page of the easy blocking position is shown in fig. 8.
The expressway congestion early warning system is provided with a display page for arranging section flow, the section flow is mainly displayed in a section flow arranging module, the section name of the front 10 of the arrangement is taken and refreshed in real time, the section name, the direction, the flow and the ranking grade of the displayed field are shown, and the first description of the ranking is the maximum section flow. The cross-sectional flow alignment page is shown in fig. 9.
The expressway congestion early warning system is provided with a display page for flow trend monitoring, wherein the flow trend monitoring is to perform flow monitoring on flow of each road section by hour (0-23 hours), and perform contrastive analysis display on yesterday and this day through a line graph. The flow trend monitoring page is shown in FIG. 10.
The expressway congestion early warning system is provided with a robot real-time pushing page, congestion information is pushed in real time through the robot, and pushed contents comprise a section name, a congestion position, a congestion index, a congestion mileage, an average vehicle speed, a section flow, a congestion reason and a congestion trend. The real-time screenshot pushed by the robot in real time is shown in fig. 11.
In addition to the preferred embodiments described above, there are other embodiments of the present invention, and various changes and modifications may be made by those skilled in the art without departing from the spirit of the present invention, which is defined in the appended claims.
Claims (4)
1. A highway congestion early warning method based on multi-data source fusion is characterized by comprising the following steps:
s1, acquiring traffic vehicle information data through a high-speed portal system, arranging two to three portal devices on a high-speed road section, wherein a section is arranged between every two adjacent portals, reading and recording information of vehicles passing through the portals in real time by the portals and uploading the information to a big data platform, analyzing by using the big data platform, processing the traffic vehicle information data acquired by the high-speed portal system in real time, and calculating section equivalent flow and section weighted vehicle speed;
s2, using a congestion discriminator to obtain the level of congestion according to the section equivalent flow and the section weighted vehicle speed calculated by the big data platform;
s3, converting the geographical longitude and latitude coordinate information into a pile number of a high-speed road section by using a high-speed geographical information engine through the association data of the portal pile number and the geographical longitude and latitude coordinates, judging the congestion possibility of a road through an algorithm by using a congestion early warning service interface, judging congestion events and congestion road conditions in an auxiliary manner, combining the congestion, accidents and construction data of the high-speed events with the reported construction information of a management center and the road congestion and accident information association congestion results, and obtaining congestion association results;
s4, pushing the congestion event to a monitoring working platform in real time, and pushing congestion information to department personnel of different levels according to the level of the congestion event;
in S1, the method for calculating the equivalent flow of the section comprises the following steps: the vehicle is divided into a first class, a second class, a third class, a fourth class, a fifth class and a sixth class according to the vehicle type, the section traffic flow of different vehicle types is converted into section equivalent flow through equivalent conversion, and the conversion rule is as follows: the equivalent transformation coefficient of a class II passenger car is 1, the equivalent transformation coefficient of a class III and class IV passenger car is 1.5, the transformation coefficient of a class I truck is 1, the transformation coefficient of a class II and class III truck is 1.5, the transformation coefficient of a class IV truck is 2.5, and the transformation coefficient of a class V and class VI truck is 4;
in S1, the method further comprises: all vehicle passing records passing through the current section are obtained from the high-speed portal system, the vehicle records counted after high-speed special service are filtered, and the section free flow is calculated: the sum of the equivalent flow of the sections converted by various vehicle types is the free flow of the section;
in S1, the section weighted vehicle speed calculation method comprises the following steps: the stake number corresponding to each portal frame represents the geographic position of the portal frame on a highway, the absolute value of the difference value of the stake numbers of two adjacent portal frames is the total length of a section, the time of a vehicle passing through the portal frame is obtained from a high-speed portal frame system, the absolute value of the time difference value of the vehicle passing through the two adjacent portal frames is the total time of the vehicle passing through the section, the passing distance is divided by the passing time to obtain the passing speed, the passing speeds of all vehicles in a time period are accumulated aiming at different vehicle types, the total number of the vehicles passing through the section in the time period is divided by the total number of the vehicles passing through the time period to obtain the average vehicle speed of the vehicle type passing through the section in the time period, the average vehicle speed obtained by accumulating different vehicle types is multiplied by the equivalent flow corresponding to the vehicle type, and then the sum of the equivalent flow of all vehicle types is divided to obtain the weighted vehicle speed of the section;
in S2, the calculation method of the congestion level includes: carrying out congestion judgment by matching the section free flow vehicle speed and the free flow vehicle flow with congestion data provided by a map operator; when the section flow is between the threshold and 1.1 times of the threshold and the section vehicle speed is between 30km/h and 60km/h, determining the first-level congestion, or determining the first-level congestion when the congestion mileage provided by a map operator is less than or equal to 2 km; when the section flow is between 1.1 time of threshold value and 1.2 times of threshold value and the section vehicle speed is between 10km/h and 30km/h, determining secondary congestion, or determining secondary congestion when the congestion mileage provided by a map operator is more than 2 kilometers and less than or equal to 5 kilometers; when the section flow is more than 1.2 times of threshold value and the section speed is less than or equal to 10km/h, judging the three-level congestion, or judging the three-level congestion when the congestion mileage provided by a map operator is more than 5 km;
and S3, judging the congestion probability of the road and the length of the road section occupied by the congestion event based on an algorithm, and converting the geographical longitude and latitude information into the stake number of the high-speed road section by combining a high-speed geographical information engine so as to assist in judging the congestion level.
2. The multi-data-source-fusion-based highway congestion warning method as claimed in claim 1, wherein in S3, the high speed event associates the stake number of the congestion position with the traffic event position to obtain the cause of the traffic congestion, and the congestion association result is obtained by combining the congestion result.
3. The multi-data-source-fusion-based highway congestion warning method as claimed in claim 1, wherein the multi-data-source-fusion-based highway congestion warning method further comprises:
a1, calculating and analyzing in real time;
a2, when judging that the first-level congestion is reached, pre-warning corresponding information to be pushed to a branch center, monitoring personnel and a shift leader, continuing program judgment, and returning to the step A1 if the condition is not met;
a3, when judging that the secondary congestion is reached, early warning corresponding information is pushed to a subsidiary master assistant and a subsidiary central master, program judgment is continued, and if the condition is not met, the step A1 is returned to;
a4, when the situation that three-level congestion is achieved is judged, early warning is carried out on corresponding messages to be pushed to a branch center principal and a center leader, the circulation is finished, and the condition is not met and the step A1 is returned;
and A5, repeatedly circulating the steps A1, A2, A3 and A4.
4. The highway congestion warning method based on multi-data source fusion as recited in claim 3,
the early warning principle is that the time point 1 reaches a threshold value, early warning is carried out once in real time, no early warning is carried out when the time point is lower than the threshold value, data in a near section are counted in real time, early warning is carried out once the detected data meet the primary congestion condition, and after information is reported out, early warning is not carried out again if the data do not meet the primary congestion condition for a period of time;
after the time point 1 is reported, if the flow fluctuates up and down at the threshold value or continuously increases and meets the first-level congestion condition, repeated early warning is carried out every 30 minutes, and the current value and the early warning value at the last time are compared and displayed during early warning.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111239862.6A CN114005276B (en) | 2021-10-25 | 2021-10-25 | Expressway congestion early warning method based on multi-data source fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111239862.6A CN114005276B (en) | 2021-10-25 | 2021-10-25 | Expressway congestion early warning method based on multi-data source fusion |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114005276A CN114005276A (en) | 2022-02-01 |
| CN114005276B true CN114005276B (en) | 2023-03-03 |
Family
ID=79923677
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111239862.6A Active CN114005276B (en) | 2021-10-25 | 2021-10-25 | Expressway congestion early warning method based on multi-data source fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114005276B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115294768A (en) * | 2022-08-02 | 2022-11-04 | 阿波罗智联(北京)科技有限公司 | Traffic jam state analysis method, device, equipment and storage medium |
| CN115359655A (en) * | 2022-08-03 | 2022-11-18 | 中远海运科技股份有限公司 | Highway monitoring system based on hierarchical clustering algorithm |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0600388D0 (en) * | 2006-01-10 | 2006-02-15 | Zarisfi Kasra | Gas, sub sea, transmission system and submersible, suspension, flexible pipeline |
| CN103280098A (en) * | 2013-05-23 | 2013-09-04 | 北京交通发展研究中心 | Traffic congestion index calculation method |
| CN104794919A (en) * | 2015-04-28 | 2015-07-22 | 辽宁省交通规划设计院 | Real-time optimal configuration method for lane resources of multi-lane expressway |
| CN204614218U (en) * | 2015-05-08 | 2015-09-02 | 济南东朔微电子有限公司 | A kind of highway second accident early warning system based on wireless location |
| CN105913029A (en) * | 2016-04-14 | 2016-08-31 | 北京中电万联科技股份有限公司 | Visitor flow data analysis, early-warning, and display system |
| CN108346288A (en) * | 2018-02-12 | 2018-07-31 | 山东路科公路信息咨询有限公司 | Section operating status method for early warning, device and electronic equipment |
| CN111508094A (en) * | 2020-06-30 | 2020-08-07 | 之江实验室 | Highway congestion finding method based on ETC portal frame and gate traffic data |
| CN112863172A (en) * | 2020-12-29 | 2021-05-28 | 千方捷通科技股份有限公司 | Highway traffic running state judgment method, early warning method, device and terminal |
| CN113178083A (en) * | 2021-03-04 | 2021-07-27 | 山东科技大学 | Congestion control method and system for multi-stage dynamic speed limit of expressway |
| CN113538898A (en) * | 2021-06-04 | 2021-10-22 | 南京美慧软件有限公司 | Multisource data-based highway congestion management and control system |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105590346B (en) * | 2016-02-18 | 2018-01-16 | 华南理工大学 | The traffic information collection of turn pike net and inducible system based on path identifying system |
| CN108320507A (en) * | 2018-03-20 | 2018-07-24 | 域通全球成都科技有限责任公司 | A kind of express highway intelligent current limiting system |
| CN111932898B (en) * | 2020-09-23 | 2020-12-22 | 南京感动科技有限公司 | ETC portal system-based short-time flow prediction method |
| CN113434619B (en) * | 2021-06-25 | 2024-06-04 | 南京领航交通科技有限公司 | 4G expressway intelligent traffic road condition monitoring system |
-
2021
- 2021-10-25 CN CN202111239862.6A patent/CN114005276B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0600388D0 (en) * | 2006-01-10 | 2006-02-15 | Zarisfi Kasra | Gas, sub sea, transmission system and submersible, suspension, flexible pipeline |
| CN103280098A (en) * | 2013-05-23 | 2013-09-04 | 北京交通发展研究中心 | Traffic congestion index calculation method |
| CN104794919A (en) * | 2015-04-28 | 2015-07-22 | 辽宁省交通规划设计院 | Real-time optimal configuration method for lane resources of multi-lane expressway |
| CN204614218U (en) * | 2015-05-08 | 2015-09-02 | 济南东朔微电子有限公司 | A kind of highway second accident early warning system based on wireless location |
| CN105913029A (en) * | 2016-04-14 | 2016-08-31 | 北京中电万联科技股份有限公司 | Visitor flow data analysis, early-warning, and display system |
| CN108346288A (en) * | 2018-02-12 | 2018-07-31 | 山东路科公路信息咨询有限公司 | Section operating status method for early warning, device and electronic equipment |
| CN111508094A (en) * | 2020-06-30 | 2020-08-07 | 之江实验室 | Highway congestion finding method based on ETC portal frame and gate traffic data |
| CN112863172A (en) * | 2020-12-29 | 2021-05-28 | 千方捷通科技股份有限公司 | Highway traffic running state judgment method, early warning method, device and terminal |
| CN113178083A (en) * | 2021-03-04 | 2021-07-27 | 山东科技大学 | Congestion control method and system for multi-stage dynamic speed limit of expressway |
| CN113538898A (en) * | 2021-06-04 | 2021-10-22 | 南京美慧软件有限公司 | Multisource data-based highway congestion management and control system |
Non-Patent Citations (1)
| Title |
|---|
| 基于可靠度的高墩大跨连续刚构桥的行车舒适性研究;万林云;《中国优秀硕士学位论文全文数据库（电子期刊）》;20131215;全文第64页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114005276A (en) | 2022-02-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114005276B (en) | Expressway congestion early warning method based on multi-data source fusion | |
| Biljecki et al. | Transportation mode-based segmentation and classification of movement trajectories | |
| Wang et al. | Visual exploration of sparse traffic trajectory data | |
| CN108345666B (en) | Vehicle abnormal track detection method based on time-space isolated points | |
| CN101739824B (en) | Data fusion technology-based traffic condition estimation method | |
| CN108877244B (en) | Method for judging operation bottleneck of bus intersection based on dynamic data | |
| CN104715610A (en) | Urban traffic-oriented traffic index calculation method | |
| CN112053558A (en) | Traffic jam state identification method, device and equipment | |
| CN110807919A (en) | Urban road network traffic operation situation evaluation method based on vehicle passing data | |
| CN102521965A (en) | Effect evaluation method of traffic demand management measures based on identification data of license plates | |
| CN114005274B (en) | Expressway toll station graded linkage management and control method based on equivalent weight of passenger car | |
| CN110400462B (en) | Track traffic passenger flow monitoring and early warning method and system based on fuzzy theory | |
| CN111210163B (en) | Public transport vehicle risk evaluation system and method based on multi-source data | |
| CN114267173B (en) | Multisource data fusion method, device and equipment for space-time characteristics of expressway | |
| CN114005275B (en) | Highway vehicle congestion judging method based on multi-data source fusion | |
| CN114363316A (en) | Intelligent networking monitoring and supervision system for cross-regional road infrastructure | |
| US20220319323A1 (en) | Method for identifying road risk based on networked vehicle-mounted adas | |
| CN114091581A (en) | Vehicle operation behavior type identification method based on sparse track | |
| CN104217591A (en) | Dynamic road condition detecting method and system | |
| Wörner et al. | Visual Analysis of Public Transport Vehicle Movement. | |
| CN113706871A (en) | Multisource data fusion system in urban traffic big data processing | |
| CN110211380B (en) | Multisource traffic data fusion expressway congestion interval detection method | |
| CN111680888A (en) | Method for determining road network capacity based on RFID data | |
| CN115409430A (en) | Logistics strength analysis method and system based on truck driving track and storage medium | |
| KR20150072470A (en) | System for analyzing dependence of spatiotemporal domain of traffic flow on the city and highway |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |